Gender: Female 
Age: 60+
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Bereaved 
First language: English 
Religion: Nothing 
Education: High school or below
Mother's education: High school or below
Father's education: Undergraduate degree
Self-assessed socioeconomic status (1-10): 3
Home: Own 
Employment: Not employed, but looking for work
Mother's occupation: Homemaker
Father's occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Hobbies: Listen to classical music;Use social media;Do arts and crafts 
Devices used daily: Tablet;Smartphone;Other;Laptop 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools they would find useful: Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative) 
Frequency of LLM use: Sometimes
LLMs used: ChatGPT 
LLM use cases: Answering questions about general knowledge 
Contexts of LLM use: Learning 
Prompt 1: How can I reduce static in house
Prompt 2: Who are some of the best Beethoven conductors including lesser-known conductors
Prompt 3: Are home humidifiers guaranteed to grow mold 
Prompt 4: which rock stars are racist
Prompt 5: which rock stars have played Israel
Prompt 6: What are the reasons behind the prominence of ugly vegetables lately
Prompt 7: What are the odds of a general strike ever happening in US and worldwide
Prompt 8: Give me some tips for working with young highly energetic dog
Prompt 9: Did Ancestry DNA keep my records even though I requested them to delete it 
Prompt 10: Why are fairies portrayed as nice than malevolent
Comments: ChatGPT is often biased in answers to US foreign policy questions.